Demystifying LLM Serving Pipeline: From Prompt to Response

Abstract

Each response from an LLM application follows a carefully optimized sequence of steps designed to balance quality, latency, and cost efficiency. This article outlines a typical LLM serving pipeline, beginning with user prompt capture, retrieval augmentation, tokenization, request routing, followed by auto-regressive token generation and post-processing to produce the final response. We evaluate critical system elements in the LLM serving pipeline, including client interfaces, policy verification mechanisms, admission control systems, KV-cache management, speculative decoding techniques, and post-processing operations. The article also examines the trade-offs among latency and throughput, memory and compute efficiency, and concurrency and response time that system architects and product leaders must balance to develop robust LLM applications.